Overview

Dataset statistics

Number of variables23
Number of observations17512
Missing cells27435
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory156.0 B

Variable types

Numeric18
Categorical5

Alerts

grade is highly correlated with bathrooms and 3 other fieldsHigh correlation
sqft_basement is highly correlated with bathrooms and 4 other fieldsHigh correlation
bathrooms is highly correlated with grade and 3 other fieldsHigh correlation
bedrooms is highly correlated with sqft_above and 1 other fieldsHigh correlation
sqft_above is highly correlated with grade and 5 other fieldsHigh correlation
sqft_living15 is highly correlated with grade and 3 other fieldsHigh correlation
floors is highly correlated with yr_builtHigh correlation
yr_renovated is highly correlated with jhygtfHigh correlation
yr_built is highly correlated with zipcode and 2 other fieldsHigh correlation
jhygtf is highly correlated with fue_renovadaHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
price is highly correlated with sqft_basementHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
sqft_living is highly correlated with grade and 5 other fieldsHigh correlation
fue_renovada is highly correlated with jhygtfHigh correlation
view is highly correlated with waterfrontHigh correlation
waterfront is highly correlated with viewHigh correlation
zipcode is highly correlated with yr_builtHigh correlation
condition is highly correlated with yr_builtHigh correlation
sqft_basement has 10655 (60.8%) missing values Missing
yr_renovated has 16780 (95.8%) missing values Missing
df_index has unique values Unique
jhygtf has 16780 (95.8%) zeros Zeros

Reproduction

Analysis started2022-10-02 00:11:03.949832
Analysis finished2022-10-02 00:12:42.648424
Duration1 minute and 38.7 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct17512
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18804.9329
Minimum1
Maximum113866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:42.911443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1134.55
Q16191.5
median14492.5
Q327229.25
95-th percentile51374.8
Maximum113866
Range113865
Interquartile range (IQR)21037.75

Descriptive statistics

Standard deviation16170.79161
Coefficient of variation (CV)0.8599228563
Kurtosis1.638998621
Mean18804.9329
Median Absolute Deviation (MAD)9638
Skewness1.270745837
Sum329311985
Variance261494501.4
MonotonicityNot monotonic
2022-10-01T19:12:43.222325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198571
 
< 0.1%
218281
 
< 0.1%
272141
 
< 0.1%
31701
 
< 0.1%
16531
 
< 0.1%
143641
 
< 0.1%
5481
 
< 0.1%
147451
 
< 0.1%
105031
 
< 0.1%
267581
 
< 0.1%
Other values (17502)17502
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
121
< 0.1%
141
< 0.1%
151
< 0.1%
ValueCountFrequency (%)
1138661
< 0.1%
1119061
< 0.1%
1095711
< 0.1%
1083111
< 0.1%
992971
< 0.1%
983251
< 0.1%
981951
< 0.1%
980151
< 0.1%
947681
< 0.1%
940831
< 0.1%

zipcode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.85838
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.5 KiB
2022-10-01T19:12:43.526482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.39133895
Coefficient of variation (CV)0.000544377088
Kurtosis-0.8484504301
Mean98077.85838
Median Absolute Deviation (MAD)42
Skewness0.4058979044
Sum1717539456
Variance2850.635074
MonotonicityNot monotonic
2022-10-01T19:12:43.829503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103492
 
2.8%
98115484
 
2.8%
98038474
 
2.7%
98052474
 
2.7%
98034453
 
2.6%
98042449
 
2.6%
98117449
 
2.6%
98006413
 
2.4%
98118409
 
2.3%
98133402
 
2.3%
Other values (60)13013
74.3%
ValueCountFrequency (%)
98001295
1.7%
98002161
 
0.9%
98003214
1.2%
98004257
1.5%
98005142
 
0.8%
98006413
2.4%
98007117
 
0.7%
98008233
1.3%
9801090
 
0.5%
98011153
 
0.9%
ValueCountFrequency (%)
98199253
1.4%
98198223
1.3%
98188108
 
0.6%
98178213
1.2%
98177203
1.2%
98168218
1.2%
98166204
1.2%
98155372
2.1%
9814851
 
0.3%
98146229
1.3%

grade
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.654179991
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.5 KiB
2022-10-01T19:12:44.078524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.170412516
Coefficient of variation (CV)0.1529115486
Kurtosis1.270700385
Mean7.654179991
Median Absolute Deviation (MAD)1
Skewness0.7758726345
Sum134040
Variance1.369865457
MonotonicityNot monotonic
2022-10-01T19:12:44.310541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
77298
41.7%
84945
28.2%
92122
 
12.1%
61635
 
9.3%
10890
 
5.1%
11315
 
1.8%
5192
 
1.1%
1276
 
0.4%
424
 
0.1%
1311
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
424
 
0.1%
5192
 
1.1%
61635
 
9.3%
77298
41.7%
84945
28.2%
92122
 
12.1%
10890
 
5.1%
11315
 
1.8%
ValueCountFrequency (%)
1311
 
0.1%
1276
 
0.4%
11315
 
1.8%
10890
 
5.1%
92122
 
12.1%
84945
28.2%
77298
41.7%
61635
 
9.3%
5192
 
1.1%
424
 
0.1%

sqft_basement
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct286
Distinct (%)4.2%
Missing10655
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean745.5457197
Minimum10
Maximum4820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:44.591218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile190
Q1450
median700
Q3980
95-th percentile1460
Maximum4820
Range4810
Interquartile range (IQR)530

Descriptive statistics

Standard deviation407.2904165
Coefficient of variation (CV)0.5462983768
Kurtosis3.894117059
Mean745.5457197
Median Absolute Deviation (MAD)260
Skewness1.149114174
Sum5112207
Variance165885.4834
MonotonicityNot monotonic
2022-10-01T19:12:44.903244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600190
 
1.1%
500178
 
1.0%
700172
 
1.0%
800163
 
0.9%
400145
 
0.8%
900122
 
0.7%
1000120
 
0.7%
300108
 
0.6%
48092
 
0.5%
53088
 
0.5%
Other values (276)5479
31.3%
(Missing)10655
60.8%
ValueCountFrequency (%)
102
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
507
 
< 0.1%
6010
 
0.1%
651
 
< 0.1%
705
 
< 0.1%
8014
0.1%
9017
0.1%
10031
0.2%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
30001
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%
26201
< 0.1%

view
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size992.0 KiB
0
15787 
2
 
775
3
 
419
1
 
280
4
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17512
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

Length

2022-10-01T19:12:45.168262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-01T19:12:45.447321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

Most occurring characters

ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17512
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common17512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015787
90.1%
2775
 
4.4%
3419
 
2.4%
1280
 
1.6%
4251
 
1.4%

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.746745089
Minimum0
Maximum8
Zeros65
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:45.666619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7324289725
Coefficient of variation (CV)0.4193107381
Kurtosis2.026467896
Mean1.746745089
Median Absolute Deviation (MAD)1
Skewness0.9094449525
Sum30589
Variance0.5364521998
MonotonicityNot monotonic
2022-10-01T19:12:45.887633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28544
48.8%
16798
38.8%
31788
 
10.2%
4264
 
1.5%
065
 
0.4%
539
 
0.2%
612
 
0.1%
82
 
< 0.1%
ValueCountFrequency (%)
065
 
0.4%
16798
38.8%
28544
48.8%
31788
 
10.2%
4264
 
1.5%
539
 
0.2%
612
 
0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
612
 
0.1%
539
 
0.2%
4264
 
1.5%
31788
 
10.2%
28544
48.8%
16798
38.8%
065
 
0.4%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.372144815
Minimum0
Maximum33
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:46.094718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9361956802
Coefficient of variation (CV)0.2776261791
Kurtosis58.60790948
Mean3.372144815
Median Absolute Deviation (MAD)1
Skewness2.269372602
Sum59053
Variance0.8764623517
MonotonicityNot monotonic
2022-10-01T19:12:46.324735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
37928
45.3%
45599
32.0%
22236
 
12.8%
51304
 
7.4%
6217
 
1.2%
1166
 
0.9%
732
 
0.2%
011
 
0.1%
811
 
0.1%
103
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
011
 
0.1%
1166
 
0.9%
22236
 
12.8%
37928
45.3%
45599
32.0%
51304
 
7.4%
6217
 
1.2%
732
 
0.2%
811
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
811
 
0.1%
732
 
0.2%
6217
 
1.2%
51304
 
7.4%
45599
32.0%
37928
45.3%

sqft_above
Real number (ℝ≥0)

HIGH CORRELATION

Distinct856
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1789.444838
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:46.597875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32220
95-th percentile3370
Maximum9410
Range9120
Interquartile range (IQR)1030

Descriptive statistics

Standard deviation825.4332172
Coefficient of variation (CV)0.4612789396
Kurtosis3.405498552
Mean1789.444838
Median Absolute Deviation (MAD)450
Skewness1.437022533
Sum31336758
Variance681339.996
MonotonicityNot monotonic
2022-10-01T19:12:46.833892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200168
 
1.0%
1300160
 
0.9%
1010159
 
0.9%
1400155
 
0.9%
1340151
 
0.9%
1220149
 
0.9%
1180146
 
0.8%
1140145
 
0.8%
1060145
 
0.8%
1100140
 
0.8%
Other values (846)15994
91.3%
ValueCountFrequency (%)
2901
 
< 0.1%
3801
 
< 0.1%
3841
 
< 0.1%
3901
 
< 0.1%
4202
< 0.1%
4301
 
< 0.1%
4401
 
< 0.1%
4702
< 0.1%
4804
< 0.1%
4902
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
66601
< 0.1%
66401
< 0.1%

sqft_living15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct722
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1985.622316
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:47.071912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32370
95-th percentile3290
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation684.3686073
Coefficient of variation (CV)0.3446620245
Kurtosis1.619937303
Mean1985.622316
Median Absolute Deviation (MAD)410
Skewness1.10646386
Sum34772218
Variance468360.3907
MonotonicityNot monotonic
2022-10-01T19:12:47.351931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1560162
 
0.9%
1440161
 
0.9%
1540157
 
0.9%
1500152
 
0.9%
1460147
 
0.8%
1580141
 
0.8%
1720141
 
0.8%
1620137
 
0.8%
1480136
 
0.8%
1520135
 
0.8%
Other values (712)16043
91.6%
ValueCountFrequency (%)
3991
 
< 0.1%
4601
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7101
 
< 0.1%
7202
 
< 0.1%
7405
< 0.1%
7502
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57905
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%
52001
 
< 0.1%

lat
Real number (ℝ≥0)

Distinct4861
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4470.201909
Minimum47.1559
Maximum47777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:47.639331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.313855
Q147.4852
median47.59465
Q347.6989
95-th percentile47559
Maximum47777
Range47729.8441
Interquartile range (IQR)0.2137

Descriptive statistics

Standard deviation13805.58766
Coefficient of variation (CV)3.088358857
Kurtosis5.848568351
Mean4470.201909
Median Absolute Deviation (MAD)0.10655
Skewness2.801391482
Sum78282175.84
Variance190594250.7
MonotonicityNot monotonic
2022-10-01T19:12:47.971468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.551814
 
0.1%
47.684214
 
0.1%
47.532214
 
0.1%
47.544513
 
0.1%
47.540213
 
0.1%
47.671113
 
0.1%
47.691613
 
0.1%
47.672713
 
0.1%
47.695513
 
0.1%
4768613
 
0.1%
Other values (4851)17379
99.2%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18081
< 0.1%
47.18531
< 0.1%
ValueCountFrequency (%)
477772
 
< 0.1%
477767
< 0.1%
477753
 
< 0.1%
477749
0.1%
477735
< 0.1%
477724
< 0.1%
477713
 
< 0.1%
477691
 
< 0.1%
477685
< 0.1%
477672
 
< 0.1%

waterfront
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size992.0 KiB
0
17378 
1
 
134

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

Length

2022-10-01T19:12:48.240489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-01T19:12:48.488727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

Most occurring characters

ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17512
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common17512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII17512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017378
99.2%
1134
 
0.8%

floors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.492947693
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:48.682741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5403790699
Coefficient of variation (CV)0.3619544559
Kurtosis-0.4743401164
Mean1.492947693
Median Absolute Deviation (MAD)0.5
Skewness0.6241077506
Sum26144.5
Variance0.2920095392
MonotonicityNot monotonic
2022-10-01T19:12:48.898758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
18681
49.6%
26639
37.9%
1.51550
 
8.9%
3497
 
2.8%
2.5138
 
0.8%
3.57
 
< 0.1%
ValueCountFrequency (%)
18681
49.6%
1.51550
 
8.9%
26639
37.9%
2.5138
 
0.8%
3497
 
2.8%
3.57
 
< 0.1%
ValueCountFrequency (%)
3.57
 
< 0.1%
3497
 
2.8%
2.5138
 
0.8%
26639
37.9%
1.51550
 
8.9%
18681
49.6%

yr_renovated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct69
Distinct (%)9.4%
Missing16780
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean1995.711749
Minimum1934
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:49.172436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1934
5-th percentile1963
Q11987
median2000
Q32008
95-th percentile2014
Maximum2015
Range81
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.91513828
Coefficient of variation (CV)0.00797466783
Kurtosis0.9341076887
Mean1995.711749
Median Absolute Deviation (MAD)10
Skewness-1.063180943
Sum1460861
Variance253.2916265
MonotonicityNot monotonic
2022-10-01T19:12:49.457457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201476
 
0.4%
201332
 
0.2%
200328
 
0.2%
200028
 
0.2%
200527
 
0.2%
200727
 
0.2%
199023
 
0.1%
200622
 
0.1%
200418
 
0.1%
200918
 
0.1%
Other values (59)433
 
2.5%
(Missing)16780
95.8%
ValueCountFrequency (%)
19341
 
< 0.1%
19402
< 0.1%
19441
 
< 0.1%
19452
< 0.1%
19462
< 0.1%
19481
 
< 0.1%
19502
< 0.1%
19511
 
< 0.1%
19533
< 0.1%
19542
< 0.1%
ValueCountFrequency (%)
201513
 
0.1%
201476
0.4%
201332
0.2%
20129
 
0.1%
20118
 
< 0.1%
201014
 
0.1%
200918
 
0.1%
200816
 
0.1%
200727
 
0.2%
200622
 
0.1%

yr_built
Real number (ℝ≥0)

HIGH CORRELATION

Distinct116
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.973561
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:49.751479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951.75
median1975
Q31997
95-th percentile2010
Maximum2015
Range115
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation29.33339767
Coefficient of variation (CV)0.01488269465
Kurtosis-0.6547921683
Mean1970.973561
Median Absolute Deviation (MAD)23
Skewness-0.4705211326
Sum34515689
Variance860.4482188
MonotonicityNot monotonic
2022-10-01T19:12:50.050502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014441
 
2.5%
2005370
 
2.1%
2006364
 
2.1%
2004357
 
2.0%
2003346
 
2.0%
2007344
 
2.0%
1977333
 
1.9%
1978326
 
1.9%
1968305
 
1.7%
2008295
 
1.7%
Other values (106)14031
80.1%
ValueCountFrequency (%)
190067
0.4%
190125
 
0.1%
190224
 
0.1%
190336
0.2%
190437
0.2%
190556
0.3%
190674
0.4%
190761
0.3%
190869
0.4%
190970
0.4%
ValueCountFrequency (%)
201531
 
0.2%
2014441
2.5%
2013158
 
0.9%
2012129
 
0.7%
2011108
 
0.6%
2010115
 
0.7%
2009182
1.0%
2008295
1.7%
2007344
2.0%
2006364
2.1%

long
Real number (ℝ)

Distinct733
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-109531.765
Minimum-122519
Maximum-121.48
Zeros0
Zeros (%)0.0%
Negative17512
Negative (%)100.0%
Memory size136.9 KiB
2022-10-01T19:12:50.322523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-122519
5-th percentile-122385.45
Q1-122318
median-122203
Q3-122059
95-th percentile-122.23
Maximum-121.48
Range122397.52
Interquartile range (IQR)259

Descriptive statistics

Standard deviation37250.64296
Coefficient of variation (CV)-0.340089863
Kurtosis4.744687022
Mean-109531.765
Median Absolute Deviation (MAD)122
Skewness2.596922767
Sum-1918120269
Variance1387610401
MonotonicityNot monotonic
2022-10-01T19:12:50.637760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.2993
 
0.5%
-122.386
 
0.5%
-12236285
 
0.5%
-12229181
 
0.5%
-12236380
 
0.5%
-122.3580
 
0.5%
-12230479
 
0.5%
-12228579
 
0.5%
-12235777
 
0.4%
-12235177
 
0.4%
Other values (723)16695
95.3%
ValueCountFrequency (%)
-1225191
 
< 0.1%
-1225141
 
< 0.1%
-1225121
 
< 0.1%
-1225112
< 0.1%
-1225091
 
< 0.1%
-1225071
 
< 0.1%
-1225061
 
< 0.1%
-1225053
< 0.1%
-1225042
< 0.1%
-1225032
< 0.1%
ValueCountFrequency (%)
-121.481
 
< 0.1%
-121.732
 
< 0.1%
-121.751
 
< 0.1%
-121.761
 
< 0.1%
-121.778
< 0.1%
-121.784
< 0.1%
-121.81
 
< 0.1%
-121.811
 
< 0.1%
-121.821
 
< 0.1%
-121.841
 
< 0.1%

jhygtf
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.42056875
Minimum0
Maximum2015
Zeros16780
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:51.302094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation399.4297204
Coefficient of variation (CV)4.788144295
Kurtosis18.97903169
Mean83.42056875
Median Absolute Deviation (MAD)0
Skewness4.579873065
Sum1460861
Variance159544.1015
MonotonicityNot monotonic
2022-10-01T19:12:51.601095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016780
95.8%
201476
 
0.4%
201332
 
0.2%
200328
 
0.2%
200028
 
0.2%
200527
 
0.2%
200727
 
0.2%
199023
 
0.1%
200622
 
0.1%
200418
 
0.1%
Other values (60)451
 
2.6%
ValueCountFrequency (%)
016780
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19452
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
ValueCountFrequency (%)
201513
 
0.1%
201476
0.4%
201332
0.2%
20129
 
0.1%
20118
 
< 0.1%
201014
 
0.1%
200918
 
0.1%
200816
 
0.1%
200727
 
0.2%
200622
 
0.1%

sqft_lot
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8441
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14943.17988
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:51.890119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1821.55
Q15026
median7620
Q310711.5
95-th percentile42782.95
Maximum1651359
Range1650839
Interquartile range (IQR)5685.5

Descriptive statistics

Standard deviation41280.84382
Coefficient of variation (CV)2.762520706
Kurtosis322.4926422
Mean14943.17988
Median Absolute Deviation (MAD)2639
Skewness13.90258241
Sum261684966
Variance1704108067
MonotonicityNot monotonic
2022-10-01T19:12:52.187160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000297
 
1.7%
6000229
 
1.3%
4000207
 
1.2%
7200177
 
1.0%
7500100
 
0.6%
480099
 
0.6%
450096
 
0.5%
840092
 
0.5%
960092
 
0.5%
360086
 
0.5%
Other values (8431)16037
91.6%
ValueCountFrequency (%)
5201
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6761
< 0.1%
6811
< 0.1%
6831
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
8816541
< 0.1%
8712001
< 0.1%
8433091
< 0.1%
7156901
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3525
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40341863.04
Minimum75000
Maximum4668000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:52.481183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1320900
median450000
Q3645000
95-th percentile1277222.5
Maximum4668000000
Range4667925000
Interquartile range (IQR)324100

Descriptive statistics

Standard deviation253858961.5
Coefficient of variation (CV)6.292693057
Kurtosis64.70307161
Mean40341863.04
Median Absolute Deviation (MAD)150000
Skewness7.368680931
Sum7.064667055 × 1011
Variance6.444437231 × 1016
MonotonicityNot monotonic
2022-10-01T19:12:52.803819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000143
 
0.8%
450000138
 
0.8%
425000132
 
0.8%
550000131
 
0.7%
500000128
 
0.7%
325000118
 
0.7%
375000117
 
0.7%
400000113
 
0.6%
300000111
 
0.6%
250000107
 
0.6%
Other values (3515)16274
92.9%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
890001
< 0.1%
ValueCountFrequency (%)
46680000001
< 0.1%
44890000001
< 0.1%
42080000001
< 0.1%
36350000001
< 0.1%
35670000001
< 0.1%
33950000001
< 0.1%
33450000001
< 0.1%
32780000001
< 0.1%
32040000001
< 0.1%
30750000001
< 0.1%

condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size992.0 KiB
3
11375 
4
4600 
5
1373 
2
 
139
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17512
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

Length

2022-10-01T19:12:53.057158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-01T19:12:53.321177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring characters

ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17512
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common17512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
311375
65.0%
44600
26.3%
51373
 
7.8%
2139
 
0.8%
125
 
0.1%

sqft_lot15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7553
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12599.49577
Minimum659
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:53.616222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum659
5-th percentile2037.65
Q15100
median7626
Q310084.5
95-th percentile36612.25
Maximum871200
Range870541
Interquartile range (IQR)4984.5

Descriptive statistics

Standard deviation26430.82805
Coefficient of variation (CV)2.097768714
Kurtosis137.4178008
Mean12599.49577
Median Absolute Deviation (MAD)2514
Skewness9.244205444
Sum220642370
Variance698588671.2
MonotonicityNot monotonic
2022-10-01T19:12:53.908243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000352
 
2.0%
4000296
 
1.7%
6000238
 
1.4%
7200176
 
1.0%
4800115
 
0.7%
7500113
 
0.6%
450097
 
0.6%
360093
 
0.5%
840091
 
0.5%
408086
 
0.5%
Other values (7543)15855
90.5%
ValueCountFrequency (%)
6591
 
< 0.1%
6601
 
< 0.1%
7481
 
< 0.1%
7503
< 0.1%
7551
 
< 0.1%
7581
 
< 0.1%
7941
 
< 0.1%
8102
< 0.1%
8863
< 0.1%
8871
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
3920402
< 0.1%
3868121
< 0.1%
3802791
< 0.1%
3600001
< 0.1%

sqft_living
Real number (ℝ≥0)

HIGH CORRELATION

Distinct939
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2081.370774
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size136.9 KiB
2022-10-01T19:12:54.186265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile935.5
Q11430
median1920
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.9428382
Coefficient of variation (CV)0.4415084758
Kurtosis5.645995735
Mean2081.370774
Median Absolute Deviation (MAD)550
Skewness1.499112814
Sum36448965
Variance844455.9399
MonotonicityNot monotonic
2022-10-01T19:12:54.498055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440113
 
0.6%
1400110
 
0.6%
1300108
 
0.6%
1480104
 
0.6%
1540103
 
0.6%
1560101
 
0.6%
1820100
 
0.6%
1720100
 
0.6%
1010100
 
0.6%
1660100
 
0.6%
Other values (929)16473
94.1%
ValueCountFrequency (%)
2901
< 0.1%
3801
< 0.1%
3841
< 0.1%
3901
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4702
< 0.1%
4802
< 0.1%
4901
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
78801
< 0.1%
78501
< 0.1%

tiene_sotano
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size992.0 KiB
0
10655 
1
6857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
010655
60.8%
16857
39.2%

Length

2022-10-01T19:12:54.764749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-01T19:12:55.007787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010655
60.8%
16857
39.2%

Most occurring characters

ValueCountFrequency (%)
010655
60.8%
16857
39.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17512
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010655
60.8%
16857
39.2%

Most occurring scripts

ValueCountFrequency (%)
Common17512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010655
60.8%
16857
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010655
60.8%
16857
39.2%

fue_renovada
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size992.0 KiB
0
16780 
1
 
732

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Length

2022-10-01T19:12:55.226804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-01T19:12:55.474823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Most occurring characters

ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17512
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common17512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016780
95.8%
1732
 
4.2%

Interactions

2022-10-01T19:12:35.088893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:12.970712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:17.717865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:22.585891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:27.551654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:32.081296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:36.904991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:42.141021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:46.731369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:51.461386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:56.539908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:01.435092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:06.379281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:11.445622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:16.057101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:20.618797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:25.741143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:30.493078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:35.378915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:13.224577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:17.977096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:22.816805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:27.767673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:32.317317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:37.195019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:42.355176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:46.924383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:51.697403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:56.787930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:01.698112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:06.628300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:11.631639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:16.255116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:20.880811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:25.942157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:30.729093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:35.716559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:13.504771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:18.263130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:23.103827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:28.086627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:32.560336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:37.490035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:42.573193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:47.206401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:52.037430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:57.093950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:01.924128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:06.895318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:11.867658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:16.486442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:21.130834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:26.231179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:30.959110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:35.960579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:13.727454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:18.528940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:23.367591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:28.323641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:32.826292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:37.743056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:42.842113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:47.462604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:52.249446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:57.354934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:02.183496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:07.170340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:12.113673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:16.742461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:21.377742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:26.496198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:31.210130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:36.213599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:14.002581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:18.804958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:23.594608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:28.597662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:33.093313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:38.009075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:43.093133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:47.717625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:52.485463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:57.616954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:02.440513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:07.430361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:12.383696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:16.901471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:21.587757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:26.759221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:31.455030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:36.484620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:14.301028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:19.089978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:23.870631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:28.866513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:33.367333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:38.301960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:43.374155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:47.970825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:52.751333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:57.870655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:02.679796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:07.680626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:12.646713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:17.171491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:21.843779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:27.067244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:31.730051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:36.780822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:14.561051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:19.342906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:24.136651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:29.134535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:33.670360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:38.579977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:43.634179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:48.222843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:52.982353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:58.203677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:02.953616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:07.956508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:12.909510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:17.421509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:22.144805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:27.312260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:31.937071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:37.051841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:14.852341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:19.605927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:24.365670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:29.365556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:33.947382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:38.806000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:43.922944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:48.466865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:53.258053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:58.495699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:03.219639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:08.199524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:13.155526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:17.675533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:22.394818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:27.566288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:32.192090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:37.317864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:15.052240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:19.886945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:24.625897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:29.632574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:34.250402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:39.070630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:44.173965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:48.734725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:53.452067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:58.780724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:03.538661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:08.462544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:13.421892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:17.928436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:22.664847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:27.827140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:32.418108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:37.589883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:15.318233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:20.141969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:25.309792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:29.881593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:34.527753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:39.301650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:44.464990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:49.056750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:53.724976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:59.006738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:03.794683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:08.746565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:13.688915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:18.154451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:22.918077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:28.078022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:32.678124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:37.873905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:15.570255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:20.380987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:25.548805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:30.136258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:34.796778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:39.967671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:44.685004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:49.365425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:53.993989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:59.271758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:04.137708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:08.982587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:13.957132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:18.438471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:23.169101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:28.326044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:32.916971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:38.151869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:15.826099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:20.685789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:25.849829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:30.367273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:35.063794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:40.247694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:44.995026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:49.763460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:54.295013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:59.577783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:04.359499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:09.235605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:14.168028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:18.720491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:23.444916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:28.627065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:33.218997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:38.453890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:16.077007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:20.948806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:26.101691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:30.587291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:35.340075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:40.521719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:45.285047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:50.049171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:54.992092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:59.852583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:04.569518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:09.894501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:14.474053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:18.987511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:24.124969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:28.905084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:33.467016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:38.719756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:16.372030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:21.215825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:26.398714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:30.841310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:35.605949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:40.797741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:45.545067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:50.348192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:55.271106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:00.153611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:04.876230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:10.162526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:14.730074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:19.271535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:24.376990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:29.156103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:33.762037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:39.417807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:16.641329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:21.464847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:26.663738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:31.129334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:35.897972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:41.072938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:45.777085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:50.564033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:55.525129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:00.427631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:05.263263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:10.446699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:14.942089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:19.549555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:24.662008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:29.426126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:34.036058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:39.684733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:16.922948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:21.754867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:26.900755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:31.379354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:36.163993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:41.338959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:46.019310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:50.856058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:55.797013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:00.670647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:05.531217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:10.740725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:15.230312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:19.828575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:24.930032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:29.693145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:34.306080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:39.947749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:17.188826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:22.046849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:27.142770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:31.636452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:36.409009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:41.603983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:46.215328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:51.095955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:56.001868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:00.912665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:05.802237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:10.929735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:15.501333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:20.117754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:25.192048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:29.958167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:34.550096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:40.154537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:17.466846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:22.304868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:27.335946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:31.886470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:36.664033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:41.856002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:46.473349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:51.268970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:11:56.254889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:01.146684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:06.093259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:11.175756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:15.774081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:20.355775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:25.460119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:30.215058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T19:12:34.817117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-01T19:12:55.707221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-01T19:12:56.183192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-01T19:12:56.618226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-01T19:12:56.936249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-01T19:12:57.239272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-01T19:12:40.614451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-01T19:12:41.602042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-01T19:12:42.079100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-01T19:12:42.331263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexzipcodegradesqft_basementviewbathroomsbedroomssqft_abovesqft_living15latwaterfrontfloorsyr_renovatedyr_builtlongjhygtfsqft_lotpriceconditionsqft_lot15sqft_livingtiene_sotanofue_renovada
0198579800610NaN02.03.02610.03140.047.553502.0NaN1993.0-122115.000.08481.0810000.0310008.02610.000
114014980338650.011.03.01560.02210.047.662101.0NaN1974.0-122189.000.08955.0685000.038976.02210.010
232909980058NaN02.04.02650.02230.047.607502.0NaN1986.0-122154.000.018295.0725000.0319856.02650.000
316305980017900.001.05.01050.01660.047.338101.0NaN1962.0-122289.000.08720.0274000.038030.01950.010
46647980117320.002.03.01310.01620.047.727501.0NaN1986.0-122232.000.06449.0445000.037429.01630.010
55865980408850.002.04.01760.02550.047.587501.0NaN1978.0-122229.000.08760.0762500.0410376.02610.010
68009980048NaN11.03.01700.02630.047.616601.0NaN1954.0-122.220.014133.0979000.0417376.01700.000
74731980118780.003.05.02090.02640.047.744902.0NaN2007.0-122192.000.04369.0540000.034610.02870.010
838480980529NaN02.04.02700.02730.047.704102.0NaN2004.0-122116.000.08810.0690000.035100.02700.000
913246980727530.001.03.01130.01260.047.762801.0NaN1976.0-122162.000.09673.0375000.039681.01660.010

Last rows

df_indexzipcodegradesqft_basementviewbathroomsbedroomssqft_abovesqft_living15latwaterfrontfloorsyr_renovatedyr_builtlongjhygtfsqft_lotpriceconditionsqft_lot15sqft_livingtiene_sotanofue_renovada
1750249558981986NaN21.02.01170.01380.047.401701.0NaN1911.0-122321.000.08925.01.750000e+0537440.01170.000
17503146981176120.001.02.0860.0980.047.676901.0NaN1918.0-122366.000.02130.04.000000e+0542800.0980.010
17504621539802412NaN04.05.06070.04680.047.595402.0NaN1999.0-121.950.0171626.01.550000e+063211267.06070.000
17505391429803410NaN22.03.02510.02560.047.705102.0NaN2006.0-122223.000.04600.01.185000e+0937500.02510.000
175069396980657NaN02.03.01950.02190.047.519402.0NaN2007.0-121869.000.07263.04.090000e+0535900.01950.000
1750714466981987NaN02.04.01780.01630.047.382802.0NaN1991.0-122302.000.06000.01.750000e+0536000.01780.000
1750830056980426NaN01.03.0840.0920.047.360701.0NaN1969.0-122085.000.05525.01.910000e+0555330.0840.000
175095824981067550.002.03.01230.01780.047.523701.0NaN1990.0-122353.000.06771.03.100000e+0536771.01780.010
1751016712980387NaN02.03.01340.01060.047.383902.0NaN1995.0-122038.000.03011.02.300000e+0533232.01340.000
175112379807510NaN02.03.03240.02970.047.585702.0NaN1994.0-122038.000.07857.08.000000e+0537857.03240.000